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Stochastic Evolution Systems: Linear Theory and Applications to Non-linear Filtering de B.L. Rozovskii

Stochastic Evolution Systems: Linear Theory and Applications to Non-linear Filtering: Mathematics and its Applications, cartea 35

Autor B.L. Rozovskii
en Limba Engleză Hardback – 31 oct 1990
Descriere de la o altă ediție sau format:
Covering the general theory of linear stochastic evolution systems with unbounded drift and diffusion operators, this book sureys Ito's second-order parabolic equations and explores filtering problems for processes whose trajectories can be described by them.
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Specificații

ISBN-13: 9780792300373
ISBN-10: 0792300378
Pagini: 315
Ilustrații: XVIII, 315 p.
Dimensiuni: 155 x 235 x 25 mm
Greutate: 0.67 kg
Ediția:1990
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Mathematics and its Applications

Locul publicării:Dordrecht, Netherlands

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Research

Cuprins

1 Examples and Auxiliary Results.- 1.0. Introduction.- 1.1. Examples of Stochastic Evolution Systems.- 1.2. Measurability and Integrability in Banach Spaces.- 1.3. Martingales in ?1.- 1.4. Diffusion Processes.- 2 Stochastic Integration in a Hilbert Space.- 2.0. Introduction.- 2.1. Martingales and Local Martingales.- 2.2. Stochastic Integrals with Respect to Square Integrable Martingale.- 2.3. Stochastic Integrable with Respect to a Local Martingale.- 2.4. An Energy Equality in a Rigged Hilbert Space.- 3 Linear Stochastic Evolution Systems in Hilbert Spaces.- 3.0. Introduction.- 3.1. Coercive Systems.- 3.2. Dissipative Systems.- 3.3. Uniqueness and the Markov Property.- 3.4. The First Boundary Problem for Ito’s Partial Differential Equations.- 4 Ito’S Second Order Parabolic Equations.- 4.0. Introduction.- 4.1. The Cauchy Problem for Superparabolic Ito’s Second Order Parabolic Equations.- 4.2. The Cauchy Problem for Ito’s Second Order Equations.- 4.3. The Forward Cauchy Problem and the Backward One in Weighted Sobolev Spaces.- 5 Ito’s Partial Differential Equations and Diffusion Processes.- 5.0. Introduction.- 5.1. The Method of Stochastic Characteristics.- 5.2. Inverse Diffusion Processes, the Method of Variation of Constants and the Liouville Equations.- 5.3. A Representation of a Density-valued Solution.- 6 Filtering Interpolation and Extrapolation of Diffusion Processes.- 6.0. Introduction.- 6.1. Bayes’ Formula and the Conditional Markov Property.- 6.2. The Forward Filtering Equation.- 6.3. The Backward Filtering Equation Interpolation and Extrapolation.- 7 Hypoellipticity of Ito’s Second Order Parabolic Equations.- 7.0. Introduction.- 7.1. Measure-valued Solution and Hypoellipticity under Generalized Hörmander’s Condition.- 7.2. The Filtering Transition Density and a Fundamental Solution of the Filtering Equation in Hypoelliptic and Superparabolic Cases.- Notes.- References.